A Model to Determine Optimal Composition of Production to Obtain Maximum Profit & Reduce Overhead Costs by Linear Programming
Why this work is in the frame
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Bibliographic record
Abstract
There are many factors which put emphasize on the importance of developing pharmaceutical industry such as human health, reduced rate of using medicines, improve healthcare to global level, influence of pharmaceutical industry on exchange market, creating job and etc. Growing improvements in production systems, appearance of mechanized systems and dynamic commercial markets have highlighted the requirements of planning. This study aims to provide a model for defining the optimal composition of production in Sobhan Darou Pharmaceutical Company to obtain maximum profits and reduce overhead costs by linear programming. Lingo application is applied to reach mentioned goals. The results showed that a mathematical planning model can be used to determine minimum of total costs and inventory control strategy. Using linear programming we can take into account all perceptible and imperceptible factors to have a choice, while output models only consider quantitative values. Another advantage of leaner programming is that it can calculate production weight and rate with a systematic method which increases the efficiency and helps to have a proper choice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it